Automated recognition of major depressive disorder from cardiovascular and respiratory physiological signals

• Published in: Frontiers in psychiatry Vol. 13; p. 970993
• Main Authors: Zitouni, M. Sami, Lih Oh, Shu, Vicnesh, Jahmunah, Khandoker, Ahsan, Acharya, U. Rajendra
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 09.12.2022
• Subjects: discrete wavelet transform; ECG; major depressive disorder; marginal fisher analysis; nonlinear features; Psychiatry; support vector machines; support vector machines; major depressive disorder; marginal fisher analysis; ECG; pulse; discrete wavelet transform; respiratory; nonlinear features
• DOI: 10.3389/fpsyt.2022.970993
• ISSN: 1664-0640

Major Depressive Disorder (MDD) is a neurohormonal disorder that causes persistent negative thoughts, mood and feelings, often accompanied with suicidal ideation (SI). Current clinical diagnostic approaches are solely based on psychiatric interview questionnaires. Thus, a computational intelligence tool for the automated detection of MDD with and without suicidal ideation is presented in this study. Since MDD is proven to affect cardiovascular and respiratory systems, the aim of the study is to automatically identify the disorder severity in MDD patients using corresponding multi-modal physiological signals, including electrocardiogram (ECG), finger photoplethysmography (PPG) and respiratory signals (RSP). Data from 88 subjects were used in this study, out of which 25 were MDD patients without SI (MDDSI−), 18 MDD patients with SI (MDDSI+), and 45 normal subjects. Multi-modal physiological signals were acquired from each subject, including ECG, RSP, and PPG signals, and then pre-processed. Discrete wavelet transform (DWT) was applied to the signals, which were decomposed up to six levels, and then eleven nonlinear features were extracted. The features were ranked according to the analysis of variance test and Marginal Fisher Analysis was employed to reduce the feature set, after which the reduced features were ranked again to select the most discriminatory features. Support vector machine with polynomial radial basis function (SVM-RBF) as well as k-nearest neighbor (KNN) classifiers were used to classify the significant features. The performance of the classifiers was evaluated in a 10-fold cross validation scheme. The best performance achieved for the classification of MDDSI+ patients was up to 85.2%, by using selected features from the obtained multi-modal signals with SVM-RBF, while it was up to 96.6% for the detection of MDD patients against healthy subjects. This work is a step toward the utilization of automated tools in diagnostics and monitoring of MDD patients in a personalized and wearable healthcare system.